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Torch: Getting started with Machine and Deep Learning培訓
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 班級規模及環境--熱線:4008699035 手機:15921673576( 微信同號) |
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每期人數限3到5人。 |
| 上課時間和地點 |
開課地址：【上海】同濟大學(滬西)/新城金郡商務樓(11號線白銀路站)【深圳分部】：電影大廈(地鐵一號線大劇院站) 【武漢分部】：佳源大廈【成都分部】：領館區1號【沈陽分部】：沈陽理工大學【鄭州分部】：錦華大廈【石家莊分部】：瑞景大廈【北京分部】：北京中山學院 【南京分部】：金港大廈
最新開班 (連續班 、周末班、晚班）：2020年3月16日 |
| 實驗設備 |
☆資深工程師授課
☆注重質量
☆邊講邊練
☆合格學員免費推薦工作
★實驗設備請點擊這兒查看★ |
| 質量保障 |
1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
2、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
3、培訓合格學員可享受免費推薦就業機會。 |
課程大綱 |
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- Introduction to Torch
- Like NumPy but with CPU and GPU implementation
Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking
Installing Torch
- Linux, Windows, Mac
Bitmapi and Docker
Installing Torch packages
- Using the LuaRocks package manager
Choosing an IDE for Torch
- ZeroBrane Studio
Eclipse plugin for Lua
Working with the Lua scripting language and LuaJIT
- Lua's integration with C/C++
Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.
Object orientation and serialization in Torch
Coding exercise
Loading a dataset in Torch
- MNIST
CIFAR-10, CIFAR-100
Imagenet
Machine Learning in Torch
- Deep Learning
Manual feature extraction vs convolutional networks
Supervised and Unsupervised Learning
Building a neural network with Torch
N-dimensional arrays
Image analysis with Torch
- Image package
The Tensor library
Working with the REPL interpreter
- Working with databases
- Networking and Torch
- GPU support in Torch
- Integrating Torch
- C, Python, and others
Embedding Torch
- iOS and Android
Other frameworks and libraries
- Facebook's optimized deep-learning modules and containers
Creating your own package
- Testing and debugging
- Releasing your application
- The future of AI and Torch
- Summary and Conclusion
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